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Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and
Benjamin P Brown1,2,3, Richard A Stein3,4, Jens Meiler1,2,3,5
1Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States.
Journal of Chemical Theory and Computation
|January 12, 2024
Summary
Artificial intelligence AlphaFold2 can approximate protein conformational probabilities. However, its contact distance distributions show peak broadening and sensitivity to mutations, limiting precise thermodynamic modeling.
Area of Science:
- Computational biology
- Protein dynamics
- Artificial intelligence in structural biology
Background:
- Protein thermodynamics governs biological functions like signal transduction and enzyme catalysis.
- Understanding protein conformational equilibria is crucial for disease treatment.
- Current computational methods are limited to small protein systems.
Purpose of the Study:
- To explore AlphaFold2's ability to approximate protein conformational Boltzmann distributions.
- To assess the utility of AlphaFold2 contact distance distributions for thermodynamic modeling.
- To investigate the impact of mutations on AlphaFold2-derived distributions.
Main Methods:
- Utilizing AlphaFold2 to generate protein conformational ensembles.
- Analyzing joint probability distributions of inter-residue contact distances.
- Comparing AlphaFold2 contact distance distributions with established methods.
Main Results:
- AlphaFold2 normalized contact distance distributions show correlation with other methods.
- Observed peak broadening in AlphaFold2 distributions compared to other methods.
- AlphaFold2 contact distance distributions demonstrated sensitivity to point mutations.
Conclusions:
- AlphaFold2 shows potential for approximating protein conformational probabilities.
- Peak broadening and mutation sensitivity require further investigation for accurate thermodynamic modeling.
- Findings offer insights for modeling thermodynamics of large biomolecular systems.
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